Forecasting foreign exchange rates with a neural-network-based fuzzy group forecasting model

In this study, a novel neural-network-based fuzzy group forecasting model is proposed for foreign exchange rates prediction. In the proposed model, some single neural network models are first used as predictors for foreign exchange rates prediction. Then these single prediction results produced by each single neural network models are fuzzified into a fuzzy prediction representation. Subsequently, these fuzzified prediction representations are aggregated into a fuzzy group consensus, i.e., aggregated prediction representation. Finally, the aggregated prediction representation is defuzzified into a crisp value as the final prediction results. For illustration and testing purposes, a typical numerical example and three typical foreign exchange rates prediction experiments are presented. Experimental results obtained reveal that the proposed neural network fuzzy group forecasting model can significantly improve the performance of foreign exchange rates forecasting.